code: def mean_absolute_percentage_error(y_true, y_pred):
      y_true, y_pred = np.array(y_true), np.array(y_pred)
      return round(
          (np.mean(np.abs((y_true - y_pred) / y_true)) * 100), 4
      )
  
imports: import os
  from copy import deepcopy
  import numpy
  import pandas
  from matplotlib import pyplot
  from sklearn.metrics import mean_squared_error, mean_absolute_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TsForecastingParams, TaskTypesEnum
label_desc: Use image node_id 1 for calling a global function with example usage: mean_absolute_percentage_error(y_true, y_pred) and returns: round